> ## Documentation Index > Fetch the complete documentation index at: https://tokenterminal.com/docs/llms.txt > Use this file to discover all available pages before exploring further. > ## Agent Instructions > To query the Token Terminal data catalog, read https://tokenterminal.com/docs/catalog/agents-manual.md first. It is the whole catalog as one page: table naming grammar, key columns, partition and cluster rules, units, additivity, and the tables that are documented but not served yet. > Never query a catalog table on a time bound alone. Also filter its cluster key, which you read from INFORMATION_SCHEMA.COLUMNS; an empty result means the object is a view, whose pruning contract is on its page. Compute is billed to the caller's own Google Cloud project. # Yields > The yield an asset pays, per asset and per deployment. A yield is a rate written as a fraction: `0.05` is five percent. `apy` is the yield over the past year, measured once per asset per day and never added up across assets, deployments or days. ## Coverage Every issuer that reports a yield, and the chains it pays on. One row per issuer.
Issuer Chains
3janeethereum
aavearbitrum, avalanche, base, bsc, ethereum, gnosis, linea, optimism, plasma, polygon, scroll, sonic, zksyncera
accountablemonad
anglearbitrum, ethereum, gnosis
anzenarbitrum, base, blast, ethereum
apyxbase, ethereum
capappethereum, katananetwork, megaeth, tempo
centrifugearbitrum, avalanche, base, bsc, celo, ethereum, monad, optimism, plumenetwork, solana, stellar
circlebsc, ethereum, near, solana
coinshiftethereum
curveethereum, xdc
dtrinitysonic
ethenaaptos, arbitrum, avalanche, base, berachain, blast, bsc, ethereum, hyperevm, linea, manta, megaeth, monad, optimism, plasma, robinhoodchain, solana, tempo, zksyncera
etherfiethereum
falconfinanceethereum
fraxstablecoinarbitrum, ethereum, fraxtal, katananetwork, seievm, sonic
fxprotocolethereum
gauntletethereum
gyroscopearbitrum, avalanche, ethereum, optimism, polygon
hastraethereum, solana
inversefinanceethereum
k3capitalethereum
kelpdaoethereum, megaeth
lidoethereum, megaeth, robinhoodchain
makerdaoarbitrum, base, ethereum, optimism, unichain
mantleethereum
maplearbitrum, base, ethereum, plasma, robinhoodchain, solana
midasbase, ethereum, monad, plasma, rootstock, starknet
morphoarbitrum, base, ethereum, hyperevm, monad, optimism, polygon, robinhoodchain, unichain, worldchain
mountainprotocolarbitrum, avalanche, base, celo, ethereum, optimism, polygon, zksyncera
nestarbitrum, ethereum, plasma, plumenetwork, solana
neutrlethereum
noonethereum, starknet, zksyncera
ondoaptos, arbitrum, ethereum, mantle, polygon, ripple, seievm, solana, stellar, sui
openedendigitalarbitrum, bsc, ethereum, ripple, solana
opentradeavalanche
paretoethereum
reavalanche, base, ethereum, tempo
resupplyethereum
rocketpoolethereum
saturnethereum
securitizeaptos, arbitrum, avalanche, bsc, ethereum, optimism, polygon, solana
sivoethereum
sparkrobinhoodchain
spikoarbitrum, base, ethereum, polygon, solana, starknet, stellar
st0xbase
stakewiseethereum
strataethereum
superstateethereum, solana
swellethereum
tenbinlabsethereum
textilecreditbase, ethereum
theoarbitrum, base, ethereum, hyperevm
tronusddbsc, ethereum
upshiftfinanceethereum
usdaiarbitrum, ethereum, plasma
usualethereum
xsybase
yieldfiarbitrum, base, ethereum, katananetwork, optimism, plumenetwork, sonic
yieldnestethereum
yieldoptimizerbase
yuzuplasma
## Tables One row per asset per day, in `metrics_assets.apy_daily`.
Column Type Description
timestampTIMESTAMPMidnight UTC of the day measured.
asset\_idSTRINGIdentifier of the asset, for example usdc. Joins dimensions.assets.
apyFLOAT64The asset's yield over the past year, written as a fraction: 0.05 means five percent. Measured once per asset per day, and never added up.
One row per deployment per day, in `metrics_asset_tokens.apy_daily`. A deployment is one token contract on one chain.
Column Type Description
timestampTIMESTAMPMidnight UTC of the day measured.
asset\_token\_idSTRINGIdentifier of the deployment, formatted \{asset\_id}-\{token\_address}-\{chain\_id}. Joins dimensions.asset\_tokens.
apyFLOAT64Yield over the past year, written as a fraction and kept within (0, 0.50]. Measured once per asset, on the deployment with the largest circulating market cap, then copied onto the asset's other deployments. Never added up.
## Sample queries Yield and supply are two tables, joined on `asset_id` at one `timestamp`. `market_cap_circulating_total` is the weight to read a rate against. ```sql theme={null} with yields as ( select asset_id, apy from `metrics_assets.apy_daily` where timestamp = timestamp('2026-08-20') ), supply as ( select asset_id, market_cap_circulating_total from `metrics_assets.market_cap_circulating_total_daily` where timestamp = timestamp('2026-08-20') ) select assets.name, assets.asset_type, yields.apy, supply.market_cap_circulating_total from yields left join supply using (asset_id) join `dimensions.assets` as assets using (asset_id) order by yields.apy desc limit 25 ``` A `full join` on `timestamp` keeps a day with a yield but no supply figure, and the reverse. Bound `timestamp` on both sides: an unbounded side reads its whole table. ```sql theme={null} with yields as ( select timestamp, apy from `metrics_assets.apy_daily` where asset_id = 'buidl' and timestamp >= timestamp('2026-06-01') ), supply as ( select timestamp, market_cap_circulating_total from `metrics_assets.market_cap_circulating_total_daily` where asset_id = 'buidl' and timestamp >= timestamp('2026-06-01') ) select timestamp, yields.apy, supply.market_cap_circulating_total from yields full join supply using (timestamp) order by timestamp ``` Each day's rates are weighted by circulating market cap, so a large asset moves the average more than a small one. `asset_type` on `dimensions.assets` is what narrows the average to one class of asset. ```sql theme={null} with yields as ( select asset_id, timestamp, apy from `metrics_assets.apy_daily` where timestamp >= timestamp('2026-07-01') ), supply as ( select asset_id, timestamp, market_cap_circulating_total from `metrics_assets.market_cap_circulating_total_daily` where timestamp >= timestamp('2026-07-01') and market_cap_circulating_total > 0 ) select yields.timestamp, sum(yields.apy * supply.market_cap_circulating_total) / sum(supply.market_cap_circulating_total) as weighted_apy from yields join supply using (asset_id, timestamp) join `dimensions.assets` as assets using (asset_id) where assets.asset_type = 'rwa' group by yields.timestamp order by yields.timestamp ``` The deployment table has `asset_token_id` alone, so the chain comes from `dimensions.asset_tokens`. ```sql theme={null} select daily.timestamp, deployments.chain_id, daily.apy from `metrics_asset_tokens.apy_daily` as daily join `dimensions.asset_tokens` as deployments using (asset_token_id) where deployments.asset_id = 'buidl' and daily.timestamp >= timestamp('2026-08-01') order by daily.timestamp, deployments.chain_id ```